Achieving sustainable biomaterials by maximising waste recovery

Sustainability assessment and prioritisation of e-waste management options in Brazil

Brazil has an increasing rate of e-waste generation, but there are currently few adequate management systems in operation, with the largest share of Waste Electrical and Electronic Equipment (WEEE) going to landfill sites or entering informal chains. The National Solid Waste Policy (2010) enforces the implementation of reverse logistics systems under the shared responsibility of consumers, companies and governments. The objective of this paper is to assess sustainability and prioritise system alternatives for potential implementation in the metropolitan region of Rio de Janeiro. Sustainability criteria and decision alternatives were defined by elicitation of stakeholders. The adopted multicriteria approach combines Life Cycle Assessment with qualitative evaluations by a small sample of regional experts with knowledge of the problem. The recommended system consists of a hybrid WEEE collection scheme with delivery points at shops, metro stations and neighbourhood centres; a pre-treatment phase with the involvement of private companies, cooperatives and social enterprises; and full recycling of all components in the country.

Characterising the composition of waste-derived fuels using a novel image analysis tool

An experimental study was completed using a previously developed and innovative image analysis approach, which has been applied here to shredded waste materials representative of waste-derived fuels. Waste components were collected from source-segregated recycling containers and shredded to <150 mm. These materials were then used to produce 3× samples of different composition. The samples were spread to represent materials on a conveyor belt, and multiple images of each sample were captured using 10×10 cm and 20×20 cm quadrats. The images were processed using ERDAS Imagine software to determine the area covered by each waste component. This coverage was converted into a mass using density data determined as part of this study, yielding a determined composition which was then compared with the known composition of the samples. The image analysis results indicated a strong correlation with the actual values (mean r=0.89). The area coverage of the sample (10×10 cm or 20×20 cm) contributes to the accuracy as the dot-grid approach used with the particle size within the samples may result in components not being sufficiently monitored. This manuscript presents initial results of the application of an adapted innovative image-based method, and critically assesses how the technique could be improved and developed in the future.